mayrajeo/Trees_sentinel.ipynb

## Trees_sentinel.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Imports"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:53.173081Z",
          "end_time": "2020-04-06T11:52:53.851704Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\n\nimport pandas as pd\nfrom pathlib import Path\nimport numpy as np\nfrom matplotlib import pyplot as plt",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": "The history saving thread hit an unexpected error (OperationalError('disk I/O error')).History will not be written to the database.\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Read data"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:53.853680Z",
          "end_time": "2020-04-06T11:52:54.287325Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "data_fdr = Path('../data')\ntrees = pd.read_excel(data_fdr/'Reflektanssit_ka_tunnukset_Evo18_100320.xlsx')",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.289493Z",
          "end_time": "2020-04-06T11:52:54.293397Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trees.rename(columns=lambda c: c.replace('.', '_'), inplace=True)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.295335Z",
          "end_time": "2020-04-06T11:52:54.299372Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trees.set_index('id', drop=True, inplace=True)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Our targets are g_x_rel, where\n* `g_1_rel` == relative proportion of basal area of Spruce\n* `g_2_rel` == relative proportion of basal area of Pine\n* `g_3_rel` == relative proportion of basal area of Birch\n* `g_5_rel` == relative proportion of basal area of Aspen\n\nRename columns so that they are readable"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.301084Z",
          "end_time": "2020-04-06T11:52:54.305906Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trees.rename(columns={'g_1_rel': 'pine_rel', 'g_2_rel': 'spruce_rel', \n                      'g_3_rel': 'birch_rel', 'g_5_rel': 'aspen_rel'}, inplace=True)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Our predicting variables are\n* `BX_mean` == mean reflectance of S2 band X\n    * similar for `max`, `min`, `stddev`"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Add some target columns"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.307484Z",
          "end_time": "2020-04-06T11:52:54.312656Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def check_dominant_species(row):\n    species = ['Spruce', 'Pine', 'Birch', 'Aspen']\n    vals = [row.spruce_rel, row.pine_rel, row.birch_rel, row.aspen_rel]\n    return species[vals.index(max(vals))]\n\ndef check_dominant_type(row):\n    species = ['Coniferous', 'Deciduous']\n    vals = [row.spruce_rel + row.pine_rel, row.birch_rel + row.aspen_rel]\n    return species[vals.index(max(vals))]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.314906Z",
          "end_time": "2020-04-06T11:52:54.382629Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trees['conif_rel'] = trees.spruce_rel + trees.pine_rel\ntrees['decid_rel'] = trees.birch_rel + trees.aspen_rel\ntrees['dom_species'] = trees.apply(lambda row: check_dominant_species(row), axis=1)\ntrees['dom_type'] = trees.apply(lambda row: check_dominant_type(row), axis=1)",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.384939Z",
          "end_time": "2020-04-06T11:52:54.388136Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "feature_cols = [c for c in trees.columns if 'B' in c]\nspecies_reg_target_cols = ['spruce_rel', 'pine_rel', 'birch_rel', 'aspen_rel']\ntypes_reg_target_cols = ['conif_rel', 'decid_rel']\nspecies_clf_target_cols = ['dom_species']\ntypes_clf_target_cols = ['dom_type']\ntarget_cols = species_reg_target_cols + types_reg_target_cols + species_clf_target_cols + types_clf_target_cols",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Quick RandomForestRegressor without even looking at data"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.390097Z",
          "end_time": "2020-04-06T11:52:54.846380Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Split randomly into train and test"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.847885Z",
          "end_time": "2020-04-06T11:52:54.857846Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_X, test_X, train_y, test_y = train_test_split(trees[feature_cols], trees[target_cols],\n                                                    test_size=0.2, random_state=42)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.859591Z",
          "end_time": "2020-04-06T11:52:54.866208Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "predictions = pd.DataFrame(columns = target_cols, index=test_y.index)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Fit RF-regressor for 4 classes"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.868307Z",
          "end_time": "2020-04-06T11:52:54.940699Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.ensemble import RandomForestRegressor",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.942314Z",
          "end_time": "2020-04-06T11:52:54.946009Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "reg_spe = RandomForestRegressor(n_estimators=500, random_state=42, max_features='log2')",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:54.948016Z",
          "end_time": "2020-04-06T11:52:55.890372Z"
        },
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "reg_spe.fit(train_X, train_y[species_reg_target_cols])",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n                      max_depth=None, max_features='log2', max_leaf_nodes=None,\n                      max_samples=None, min_impurity_decrease=0.0,\n                      min_impurity_split=None, min_samples_leaf=1,\n                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n                      n_estimators=500, n_jobs=None, oob_score=False,\n                      random_state=42, verbose=0, warm_start=False)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:55.892168Z",
          "end_time": "2020-04-06T11:52:55.936895Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "preds = reg_spe.predict(test_X)\nfor i in range(4):\n    predictions[species_reg_target_cols[i]] = preds[:,i]",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:55.939157Z",
          "end_time": "2020-04-06T11:52:55.941857Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import r2_score, mean_squared_error",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Calculate R2 score for all data (both with variance_weighted and uniform_average) and for each species individually. Larger is better."
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:55.944593Z",
          "end_time": "2020-04-06T11:52:55.963015Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "r2_score(test_y[species_reg_target_cols], predictions[species_reg_target_cols], multioutput='variance_weighted')",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "0.46437865395943956"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:55.965723Z",
          "end_time": "2020-04-06T11:52:55.980646Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "r2_score(test_y[species_reg_target_cols], predictions[species_reg_target_cols], multioutput='uniform_average')",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "0.351622302066006"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:55.985214Z",
          "end_time": "2020-04-06T11:52:55.989327Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "species = ['Spruce', 'Pine', 'Birch', 'Aspen']",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:55.991585Z",
          "end_time": "2020-04-06T11:52:56.001629Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "r2_raw = r2_score(test_y[species_reg_target_cols], predictions[species_reg_target_cols], multioutput='raw_values')\nfor i in range(4):\n    print(f'R2 score for {species[i]} is {r2_raw[i]}')",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": "R2 score for Spruce is 0.4842798365920633\nR2 score for Pine is 0.536230088750485\nR2 score for Birch is 0.37559986436506076\nR2 score for Aspen is 0.010379418556414777\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Do the same for RMSE, smaller is better."
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:56.003678Z",
          "end_time": "2020-04-06T11:52:56.010402Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "mean_squared_error(test_y[species_reg_target_cols], predictions[species_reg_target_cols], squared=False)",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "0.20081410442855663"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:56.015553Z",
          "end_time": "2020-04-06T11:52:56.031442Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "for i in range(4):\n    print(f\"\"\"RMSE for {species[i]} is {mean_squared_error(test_y[species_reg_target_cols].values[:,i], \n                                        predictions[species_reg_target_cols].values[:,i], squared=False)}\"\"\")",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": "RMSE for Spruce is 0.2216693131497892\nRMSE for Pine is 0.25924179124046404\nRMSE for Birch is 0.16209727121748924\nRMSE for Aspen is 0.13669711809600482\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Plot results"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:56.037121Z",
          "end_time": "2020-04-06T11:52:56.676237Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, axs = plt.subplots(2,2, figsize=(12,12))\nx = np.linspace(0,1,10)\ny = x\nfor i, ax in zip(range(4), axs.flatten()):\n    ax.plot(x,y, color='r')\n    ax.scatter(test_y[species_reg_target_cols].values[:,i], \n               predictions[species_reg_target_cols].values[:,i])\n    ax.plot()\n    ax.set_title(species[i])\n    ax.set_xlabel('True')\n    ax.set_ylabel('Predicted')\n    ax.grid(True)",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x864 with 4 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Do the same for coniferous/deciduous"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:56.678565Z",
          "end_time": "2020-04-06T11:52:57.516464Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "reg_typ = RandomForestRegressor(n_estimators=500, random_state=42, max_features='log2')\nreg_typ.fit(train_X, train_y[types_reg_target_cols])",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n                      max_depth=None, max_features='log2', max_leaf_nodes=None,\n                      max_samples=None, min_impurity_decrease=0.0,\n                      min_impurity_split=None, min_samples_leaf=1,\n                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n                      n_estimators=500, n_jobs=None, oob_score=False,\n                      random_state=42, verbose=0, warm_start=False)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.518354Z",
          "end_time": "2020-04-06T11:52:57.575013Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "preds = reg_typ.predict(test_X)\nfor i in range(2):\n    predictions[types_reg_target_cols[i]] = preds[:,i]",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.576962Z",
          "end_time": "2020-04-06T11:52:57.587506Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "r2_score(test_y[types_reg_target_cols], predictions[types_reg_target_cols], multioutput='variance_weighted')",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "0.4303069253561085"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.590084Z",
          "end_time": "2020-04-06T11:52:57.599032Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "r2_score(test_y[types_reg_target_cols], predictions[types_reg_target_cols], multioutput='uniform_average')",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "0.42720313727345144"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.601107Z",
          "end_time": "2020-04-06T11:52:57.610157Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "types = ['Coniferous', 'Deciduous']\nr2_raw = r2_score(test_y[types_reg_target_cols], predictions[types_reg_target_cols], multioutput='raw_values')\nfor i in range(2):\n    print(f'R2 score for {types[i]} is {r2_raw[i]}')",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": "R2 score for Coniferous is 0.47009233673880113\nR2 score for Deciduous is 0.38431393780810175\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.612293Z",
          "end_time": "2020-04-06T11:52:57.620261Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "mean_squared_error(test_y[types_reg_target_cols], predictions[types_reg_target_cols], squared=False)",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "0.18941044128867343"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.623434Z",
          "end_time": "2020-04-06T11:52:57.633174Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "for i in range(2):\n     print(f\"\"\"RMSE for {types[i]} is {mean_squared_error(test_y[types_reg_target_cols].values[:,i], \n                                        predictions[types_reg_target_cols].values[:,i], squared=False)}\"\"\")",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": "RMSE for Coniferous is 0.18917136005834667\nRMSE for Deciduous is 0.18964922112156266\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.635821Z",
          "end_time": "2020-04-06T11:52:57.917424Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(1,2, figsize=(12,5))\nx = np.linspace(0,1,10)\ny = x\nfor i in range(2):\n    ax[i].plot(x,y, color='r')\n    ax[i].scatter(test_y[types_reg_target_cols].values[:,i], \n               predictions[types_reg_target_cols].values[:,i])\n    ax[i].plot()\n    ax[i].set_title(types[i])\n    ax[i].set_xlabel('True')\n    ax[i].set_ylabel('Predicted')\n    ax[i].grid(True)",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x360 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Look more closely at data"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Check the dominant species for each plot"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.919004Z",
          "end_time": "2020-04-06T11:52:57.925703Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trees.dom_species.value_counts()",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "Pine      175\nSpruce    154\nBirch      67\nAspen       9\nName: dom_species, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "As expected, aspen is vastly underrepresented. Check for dominant type (coniferous or deciduous)"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.927837Z",
          "end_time": "2020-04-06T11:52:57.937257Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trees.aspen_rel.describe()",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "count    405.000000\nmean       0.026104\nstd        0.109405\nmin        0.000000\n25%        0.000000\n50%        0.000000\n75%        0.000000\nmax        0.848000\nName: aspen_rel, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.939204Z",
          "end_time": "2020-04-06T11:52:57.944531Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "trees.dom_type.value_counts()",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": "Coniferous    337\nDeciduous      68\nName: dom_type, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Plot some graphs"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:57.946480Z",
          "end_time": "2020-04-06T11:52:58.509968Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, axs = plt.subplots(2,2, figsize=(12,12))\nfor i, ax, col, in zip(range(4), axs.flatten(), ['spruce_rel', 'pine_rel', 'birch_rel', 'aspen_rel']):\n    ax.scatter(np.linspace(0,1.,21),[len(trees[trees[col] >= i]) for i in np.linspace(0,1.,21)])\n    ax.grid(True)\n    ax.set_title(f'Relative % counts for {species[i]}')\n    ax.set_xlabel('Minimum %')\n    ax.set_ylabel('Count')",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x864 with 4 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:58.511841Z",
          "end_time": "2020-04-06T11:52:58.816102Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, axs = plt.subplots(1,2, figsize=(12,6))\nfor i, ax, col, in zip(range(2), axs.flatten(), ['conif_rel', 'decid_rel']):\n    ax.scatter(np.linspace(0,1.0,21),[len(trees[trees[col] >= i]) for i in np.linspace(0,1.0,21)])\n    ax.grid(True)\n    ax.set_title(f'Relative % counts for {types[i]}')\n    ax.set_xlabel('Minimum %')\n    ax.set_ylabel('Count')",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "These are kind of histograms"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:58.817578Z",
          "end_time": "2020-04-06T11:52:59.318618Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, axs = plt.subplots(2,2, figsize=(12,12))\nfor i, ax, col, in zip(range(4), axs.flatten(), ['spruce_rel', 'pine_rel', 'birch_rel', 'aspen_rel']):\n    ax.plot( np.linspace(0.025,0.975,20),[len(trees[trees[col].between(i-0.025, i+0.025)]) for i in np.linspace(0.025,0.975,20)])\n    ax.grid(True)\n    ax.set_title(f'5% bin histogram for {species[i]}')\n    ax.set_xlabel('%')\n    ax.set_ylabel('Count')",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x864 with 4 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:59.320325Z",
          "end_time": "2020-04-06T11:52:59.574003Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, axs = plt.subplots(1,2, figsize=(12,6))\nfor i, ax, col, in zip(range(2), axs.flatten(), ['conif_rel', 'decid_rel']):\n    ax.plot(np.linspace(0.025,0.975,20),[len(trees[trees[col].between(i-0.025, i+0.025)]) for i in np.linspace(0.025,0.975,20)])\n    ax.grid(True)\n    ax.set_title(f'5% bin histogram for {types[i]}')\n    ax.set_xlabel('%')\n    ax.set_ylabel('Count')",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 864x432 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Try to classify dominant type"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:59.575747Z",
          "end_time": "2020-04-06T11:52:59.578288Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.ensemble import RandomForestClassifier",
      "execution_count": 39,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:59.580043Z",
          "end_time": "2020-04-06T11:52:59.582997Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "clf_typ = RandomForestClassifier(n_estimators=500, random_state=42, max_features='log2')",
      "execution_count": 40,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:52:59.584792Z",
          "end_time": "2020-04-06T11:53:00.325556Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "clf_typ.fit(train_X, train_y[types_clf_target_cols].values.ravel())",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n                       criterion='gini', max_depth=None, max_features='log2',\n                       max_leaf_nodes=None, max_samples=None,\n                       min_impurity_decrease=0.0, min_impurity_split=None,\n                       min_samples_leaf=1, min_samples_split=2,\n                       min_weight_fraction_leaf=0.0, n_estimators=500,\n                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n                       warm_start=False)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:00.327287Z",
          "end_time": "2020-04-06T11:53:00.331123Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import classification_report, plot_confusion_matrix",
      "execution_count": 42,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:00.332814Z",
          "end_time": "2020-04-06T11:53:00.382622Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "predictions['dom_type'] = clf_typ.predict(test_X)",
      "execution_count": 43,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:00.384097Z",
          "end_time": "2020-04-06T11:53:00.430791Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "typ_probas = ['conif_proba', 'decid_proba']\nprobas = clf_typ.predict_proba(test_X)\nfor i in range(2):\n    predictions[typ_probas[i]] = probas[:,i]",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:00.432408Z",
          "end_time": "2020-04-06T11:53:00.442017Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(classification_report(test_y[types_clf_target_cols], predictions[types_clf_target_cols]))",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n  Coniferous       0.92      0.96      0.94        70\n   Deciduous       0.62      0.45      0.53        11\n\n    accuracy                           0.89        81\n   macro avg       0.77      0.71      0.73        81\nweighted avg       0.88      0.89      0.88        81\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And then same for the dominant species"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:00.443396Z",
          "end_time": "2020-04-06T11:53:01.409499Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "clf_spe = RandomForestClassifier(n_estimators=500, random_state=42, max_features='log2')\nclf_spe.fit(train_X, train_y[species_clf_target_cols].values.ravel())\npredictions['dom_species'] = clf_spe.predict(test_X)\nspe_probas = ['spruce_proba', 'pine_proba', 'birch_proba', 'aspen_proba']\nprobas = clf_spe.predict_proba(test_X)\nfor i in range(4):\n    predictions[spe_probas[i]] = probas[:,-(i+1)] # probas are returned in alphabetical order\nprint(classification_report(test_y[species_clf_target_cols], predictions[species_clf_target_cols]))",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n       Aspen       0.00      0.00      0.00         2\n       Birch       0.33      0.33      0.33         9\n        Pine       0.87      0.77      0.82        44\n      Spruce       0.58      0.73      0.64        26\n\n    accuracy                           0.69        81\n   macro avg       0.45      0.46      0.45        81\nweighted avg       0.70      0.69      0.69        81\n\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "/home/mayrajeo/miniconda3/envs/fastai2/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n  _warn_prf(average, modifier, msg_start, len(result))\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:01.411758Z",
          "end_time": "2020-04-06T11:53:01.633053Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plot_confusion_matrix(clf_spe, test_X, test_y[species_clf_target_cols].values.ravel())",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/plain": "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4fc5ce7cd0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Examine results"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:01.635055Z",
          "end_time": "2020-04-06T11:53:01.655644Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "predictions.head(10)",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": "      spruce_rel  pine_rel  birch_rel  aspen_rel  conif_rel  decid_rel  \\\nid                                                                       \n78      0.763874  0.167498   0.058240   0.009084   0.954728   0.043998   \n1120    0.123466  0.776796   0.059480   0.001596   0.917412   0.049204   \n1287    0.144546  0.475010   0.147656   0.043364   0.677382   0.164208   \n36      0.749530  0.184704   0.049712   0.012126   0.947226   0.049596   \n46      0.636778  0.270408   0.070644   0.009988   0.934466   0.051146   \n86      0.131072  0.816556   0.041412   0.004676   0.947020   0.047509   \n1039    0.692274  0.123000   0.153034   0.017088   0.808606   0.174612   \n1234    0.176730  0.757610   0.048694   0.004246   0.955388   0.035925   \n1266    0.171182  0.686284   0.112584   0.001372   0.850848   0.119737   \n1167    0.300038  0.531842   0.094828   0.001472   0.828094   0.111223   \n\n     dom_species    dom_type  conif_proba  decid_proba  spruce_proba  \\\nid                                                                     \n78        Spruce  Coniferous        1.000        0.000         0.956   \n1120        Pine  Coniferous        0.996        0.004         0.016   \n1287        Pine  Coniferous        0.804        0.196         0.322   \n36        Spruce  Coniferous        0.988        0.012         0.814   \n46        Spruce  Coniferous        1.000        0.000         0.810   \n86          Pine  Coniferous        0.998        0.002         0.014   \n1039      Spruce  Coniferous        0.964        0.036         0.920   \n1234        Pine  Coniferous        0.996        0.004         0.010   \n1266        Pine  Coniferous        0.896        0.104         0.130   \n1167        Pine  Coniferous        0.966        0.034         0.330   \n\n      pine_proba  birch_proba  aspen_proba  \nid                                          \n78         0.036        0.006        0.002  \n1120       0.968        0.016        0.000  \n1287       0.476        0.160        0.042  \n36         0.176        0.010        0.000  \n46         0.178        0.010        0.002  \n86         0.982        0.002        0.002  \n1039       0.066        0.014        0.000  \n1234       0.974        0.016        0.000  \n1266       0.798        0.064        0.008  \n1167       0.598        0.066        0.006  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>spruce_rel</th>\n      <th>pine_rel</th>\n      <th>birch_rel</th>\n      <th>aspen_rel</th>\n      <th>conif_rel</th>\n      <th>decid_rel</th>\n      <th>dom_species</th>\n      <th>dom_type</th>\n      <th>conif_proba</th>\n      <th>decid_proba</th>\n      <th>spruce_proba</th>\n      <th>pine_proba</th>\n      <th>birch_proba</th>\n      <th>aspen_proba</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>78</th>\n      <td>0.763874</td>\n      <td>0.167498</td>\n      <td>0.058240</td>\n      <td>0.009084</td>\n      <td>0.954728</td>\n      <td>0.043998</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.956</td>\n      <td>0.036</td>\n      <td>0.006</td>\n      <td>0.002</td>\n    </tr>\n    <tr>\n      <th>1120</th>\n      <td>0.123466</td>\n      <td>0.776796</td>\n      <td>0.059480</td>\n      <td>0.001596</td>\n      <td>0.917412</td>\n      <td>0.049204</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.996</td>\n      <td>0.004</td>\n      <td>0.016</td>\n      <td>0.968</td>\n      <td>0.016</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>0.144546</td>\n      <td>0.475010</td>\n      <td>0.147656</td>\n      <td>0.043364</td>\n      <td>0.677382</td>\n      <td>0.164208</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.804</td>\n      <td>0.196</td>\n      <td>0.322</td>\n      <td>0.476</td>\n      <td>0.160</td>\n      <td>0.042</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>0.749530</td>\n      <td>0.184704</td>\n      <td>0.049712</td>\n      <td>0.012126</td>\n      <td>0.947226</td>\n      <td>0.049596</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.988</td>\n      <td>0.012</td>\n      <td>0.814</td>\n      <td>0.176</td>\n      <td>0.010</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>0.636778</td>\n      <td>0.270408</td>\n      <td>0.070644</td>\n      <td>0.009988</td>\n      <td>0.934466</td>\n      <td>0.051146</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.810</td>\n      <td>0.178</td>\n      <td>0.010</td>\n      <td>0.002</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>0.131072</td>\n      <td>0.816556</td>\n      <td>0.041412</td>\n      <td>0.004676</td>\n      <td>0.947020</td>\n      <td>0.047509</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.998</td>\n      <td>0.002</td>\n      <td>0.014</td>\n      <td>0.982</td>\n      <td>0.002</td>\n      <td>0.002</td>\n    </tr>\n    <tr>\n      <th>1039</th>\n      <td>0.692274</td>\n      <td>0.123000</td>\n      <td>0.153034</td>\n      <td>0.017088</td>\n      <td>0.808606</td>\n      <td>0.174612</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.964</td>\n      <td>0.036</td>\n      <td>0.920</td>\n      <td>0.066</td>\n      <td>0.014</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>1234</th>\n      <td>0.176730</td>\n      <td>0.757610</td>\n      <td>0.048694</td>\n      <td>0.004246</td>\n      <td>0.955388</td>\n      <td>0.035925</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.996</td>\n      <td>0.004</td>\n      <td>0.010</td>\n      <td>0.974</td>\n      <td>0.016</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>1266</th>\n      <td>0.171182</td>\n      <td>0.686284</td>\n      <td>0.112584</td>\n      <td>0.001372</td>\n      <td>0.850848</td>\n      <td>0.119737</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.896</td>\n      <td>0.104</td>\n      <td>0.130</td>\n      <td>0.798</td>\n      <td>0.064</td>\n      <td>0.008</td>\n    </tr>\n    <tr>\n      <th>1167</th>\n      <td>0.300038</td>\n      <td>0.531842</td>\n      <td>0.094828</td>\n      <td>0.001472</td>\n      <td>0.828094</td>\n      <td>0.111223</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.966</td>\n      <td>0.034</td>\n      <td>0.330</td>\n      <td>0.598</td>\n      <td>0.066</td>\n      <td>0.006</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:01.657611Z",
          "end_time": "2020-04-06T11:53:01.676550Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_y.head(10)",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 49,
          "data": {
            "text/plain": "      spruce_rel  pine_rel  birch_rel  aspen_rel  conif_rel  decid_rel  \\\nid                                                                       \n78         0.760     0.221      0.019        0.0      0.981      0.019   \n1120       0.069     0.894      0.037        0.0      0.963      0.037   \n1287       0.000     0.901      0.099        0.0      0.901      0.099   \n36         0.858     0.000      0.142        0.0      0.858      0.142   \n46         0.730     0.124      0.146        0.0      0.854      0.146   \n86         0.290     0.630      0.080        0.0      0.920      0.080   \n1039       0.391     0.161      0.430        0.0      0.552      0.430   \n1234       0.013     0.987      0.000        0.0      1.000      0.000   \n1266       0.000     1.000      0.000        0.0      1.000      0.000   \n1167       0.126     0.800      0.073        0.0      0.926      0.073   \n\n     dom_species    dom_type  \nid                            \n78        Spruce  Coniferous  \n1120        Pine  Coniferous  \n1287        Pine  Coniferous  \n36        Spruce  Coniferous  \n46        Spruce  Coniferous  \n86          Pine  Coniferous  \n1039       Birch  Coniferous  \n1234        Pine  Coniferous  \n1266        Pine  Coniferous  \n1167        Pine  Coniferous  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>spruce_rel</th>\n      <th>pine_rel</th>\n      <th>birch_rel</th>\n      <th>aspen_rel</th>\n      <th>conif_rel</th>\n      <th>decid_rel</th>\n      <th>dom_species</th>\n      <th>dom_type</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>78</th>\n      <td>0.760</td>\n      <td>0.221</td>\n      <td>0.019</td>\n      <td>0.0</td>\n      <td>0.981</td>\n      <td>0.019</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>1120</th>\n      <td>0.069</td>\n      <td>0.894</td>\n      <td>0.037</td>\n      <td>0.0</td>\n      <td>0.963</td>\n      <td>0.037</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>0.000</td>\n      <td>0.901</td>\n      <td>0.099</td>\n      <td>0.0</td>\n      <td>0.901</td>\n      <td>0.099</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>0.858</td>\n      <td>0.000</td>\n      <td>0.142</td>\n      <td>0.0</td>\n      <td>0.858</td>\n      <td>0.142</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>0.730</td>\n      <td>0.124</td>\n      <td>0.146</td>\n      <td>0.0</td>\n      <td>0.854</td>\n      <td>0.146</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>0.290</td>\n      <td>0.630</td>\n      <td>0.080</td>\n      <td>0.0</td>\n      <td>0.920</td>\n      <td>0.080</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>1039</th>\n      <td>0.391</td>\n      <td>0.161</td>\n      <td>0.430</td>\n      <td>0.0</td>\n      <td>0.552</td>\n      <td>0.430</td>\n      <td>Birch</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>1234</th>\n      <td>0.013</td>\n      <td>0.987</td>\n      <td>0.000</td>\n      <td>0.0</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>1266</th>\n      <td>0.000</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.0</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n    </tr>\n    <tr>\n      <th>1167</th>\n      <td>0.126</td>\n      <td>0.800</td>\n      <td>0.073</td>\n      <td>0.0</td>\n      <td>0.926</td>\n      <td>0.073</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:01.681233Z",
          "end_time": "2020-04-06T11:53:01.684402Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.set_option('display.max_columns', 40)",
      "execution_count": 50,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:01.686408Z",
          "end_time": "2020-04-06T11:53:01.727149Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "combined = test_y.join(predictions, how='left', rsuffix='_pred')\ncombined.head()",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 51,
          "data": {
            "text/plain": "      spruce_rel  pine_rel  birch_rel  aspen_rel  conif_rel  decid_rel  \\\nid                                                                       \n78         0.760     0.221      0.019        0.0      0.981      0.019   \n1120       0.069     0.894      0.037        0.0      0.963      0.037   \n1287       0.000     0.901      0.099        0.0      0.901      0.099   \n36         0.858     0.000      0.142        0.0      0.858      0.142   \n46         0.730     0.124      0.146        0.0      0.854      0.146   \n\n     dom_species    dom_type  spruce_rel_pred  pine_rel_pred  birch_rel_pred  \\\nid                                                                             \n78        Spruce  Coniferous         0.763874       0.167498        0.058240   \n1120        Pine  Coniferous         0.123466       0.776796        0.059480   \n1287        Pine  Coniferous         0.144546       0.475010        0.147656   \n36        Spruce  Coniferous         0.749530       0.184704        0.049712   \n46        Spruce  Coniferous         0.636778       0.270408        0.070644   \n\n      aspen_rel_pred  conif_rel_pred  decid_rel_pred dom_species_pred  \\\nid                                                                      \n78          0.009084        0.954728        0.043998           Spruce   \n1120        0.001596        0.917412        0.049204             Pine   \n1287        0.043364        0.677382        0.164208             Pine   \n36          0.012126        0.947226        0.049596           Spruce   \n46          0.009988        0.934466        0.051146           Spruce   \n\n     dom_type_pred  conif_proba  decid_proba  spruce_proba  pine_proba  \\\nid                                                                       \n78      Coniferous        1.000        0.000         0.956       0.036   \n1120    Coniferous        0.996        0.004         0.016       0.968   \n1287    Coniferous        0.804        0.196         0.322       0.476   \n36      Coniferous        0.988        0.012         0.814       0.176   \n46      Coniferous        1.000        0.000         0.810       0.178   \n\n      birch_proba  aspen_proba  \nid                              \n78          0.006        0.002  \n1120        0.016        0.000  \n1287        0.160        0.042  \n36          0.010        0.000  \n46          0.010        0.002  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>spruce_rel</th>\n      <th>pine_rel</th>\n      <th>birch_rel</th>\n      <th>aspen_rel</th>\n      <th>conif_rel</th>\n      <th>decid_rel</th>\n      <th>dom_species</th>\n      <th>dom_type</th>\n      <th>spruce_rel_pred</th>\n      <th>pine_rel_pred</th>\n      <th>birch_rel_pred</th>\n      <th>aspen_rel_pred</th>\n      <th>conif_rel_pred</th>\n      <th>decid_rel_pred</th>\n      <th>dom_species_pred</th>\n      <th>dom_type_pred</th>\n      <th>conif_proba</th>\n      <th>decid_proba</th>\n      <th>spruce_proba</th>\n      <th>pine_proba</th>\n      <th>birch_proba</th>\n      <th>aspen_proba</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>78</th>\n      <td>0.760</td>\n      <td>0.221</td>\n      <td>0.019</td>\n      <td>0.0</td>\n      <td>0.981</td>\n      <td>0.019</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.763874</td>\n      <td>0.167498</td>\n      <td>0.058240</td>\n      <td>0.009084</td>\n      <td>0.954728</td>\n      <td>0.043998</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.956</td>\n      <td>0.036</td>\n      <td>0.006</td>\n      <td>0.002</td>\n    </tr>\n    <tr>\n      <th>1120</th>\n      <td>0.069</td>\n      <td>0.894</td>\n      <td>0.037</td>\n      <td>0.0</td>\n      <td>0.963</td>\n      <td>0.037</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.123466</td>\n      <td>0.776796</td>\n      <td>0.059480</td>\n      <td>0.001596</td>\n      <td>0.917412</td>\n      <td>0.049204</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.996</td>\n      <td>0.004</td>\n      <td>0.016</td>\n      <td>0.968</td>\n      <td>0.016</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>1287</th>\n      <td>0.000</td>\n      <td>0.901</td>\n      <td>0.099</td>\n      <td>0.0</td>\n      <td>0.901</td>\n      <td>0.099</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.144546</td>\n      <td>0.475010</td>\n      <td>0.147656</td>\n      <td>0.043364</td>\n      <td>0.677382</td>\n      <td>0.164208</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.804</td>\n      <td>0.196</td>\n      <td>0.322</td>\n      <td>0.476</td>\n      <td>0.160</td>\n      <td>0.042</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>0.858</td>\n      <td>0.000</td>\n      <td>0.142</td>\n      <td>0.0</td>\n      <td>0.858</td>\n      <td>0.142</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.749530</td>\n      <td>0.184704</td>\n      <td>0.049712</td>\n      <td>0.012126</td>\n      <td>0.947226</td>\n      <td>0.049596</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.988</td>\n      <td>0.012</td>\n      <td>0.814</td>\n      <td>0.176</td>\n      <td>0.010</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>0.730</td>\n      <td>0.124</td>\n      <td>0.146</td>\n      <td>0.0</td>\n      <td>0.854</td>\n      <td>0.146</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.636778</td>\n      <td>0.270408</td>\n      <td>0.070644</td>\n      <td>0.009988</td>\n      <td>0.934466</td>\n      <td>0.051146</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.810</td>\n      <td>0.178</td>\n      <td>0.010</td>\n      <td>0.002</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:01.730072Z",
          "end_time": "2020-04-06T11:53:01.777027Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "combined[(combined.dom_species != combined.dom_species_pred) | (combined.dom_type != combined.dom_type_pred)].head(22)",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 52,
          "data": {
            "text/plain": "      spruce_rel  pine_rel  birch_rel  aspen_rel  conif_rel  decid_rel  \\\nid                                                                       \n1039       0.391     0.161      0.430      0.000      0.552      0.430   \n1267       0.383     0.000      0.572      0.000      0.383      0.572   \n1289       0.339     0.109      0.302      0.216      0.448      0.518   \n1079       0.031     0.000      0.151      0.799      0.031      0.950   \n1254       0.151     0.645      0.188      0.000      0.796      0.188   \n1097       0.546     0.000      0.454      0.000      0.546      0.454   \n1074       0.162     0.539      0.299      0.000      0.701      0.299   \n1034       0.846     0.000      0.154      0.000      0.846      0.154   \n1152       0.265     0.385      0.350      0.000      0.650      0.350   \n1253       0.147     0.000      0.145      0.000      0.147      0.145   \n1168       0.000     0.396      0.604      0.000      0.396      0.604   \n1219       0.028     0.792      0.180      0.000      0.820      0.180   \n1042       0.137     0.461      0.084      0.000      0.598      0.084   \n1239       0.269     0.189      0.322      0.000      0.458      0.322   \n1027       0.129     0.570      0.299      0.000      0.699      0.299   \n1295       0.539     0.346      0.115      0.000      0.885      0.115   \n1244       0.048     0.446      0.423      0.060      0.494      0.483   \n33         0.388     0.033      0.509      0.070      0.421      0.579   \n1094       0.516     0.000      0.484      0.000      0.516      0.484   \n1304       0.344     0.000      0.229      0.205      0.344      0.434   \n1005       0.029     0.813      0.156      0.000      0.842      0.156   \n1238       0.000     1.000      0.000      0.000      1.000      0.000   \n\n     dom_species    dom_type  spruce_rel_pred  pine_rel_pred  birch_rel_pred  \\\nid                                                                             \n1039       Birch  Coniferous         0.692274       0.123000        0.153034   \n1267       Birch   Deciduous         0.501784       0.131618        0.232284   \n1289      Spruce   Deciduous         0.213932       0.197364        0.467649   \n1079       Aspen   Deciduous         0.346982       0.335906        0.202276   \n1254        Pine  Coniferous         0.550202       0.293982        0.125520   \n1097      Spruce  Coniferous         0.353554       0.096138        0.480882   \n1074        Pine  Coniferous         0.758162       0.168366        0.058364   \n1034      Spruce  Coniferous         0.400772       0.390150        0.158930   \n1152        Pine  Coniferous         0.367260       0.129524        0.309858   \n1253      Spruce  Coniferous         0.362770       0.091748        0.357904   \n1168       Birch   Deciduous         0.613394       0.211012        0.094128   \n1219        Pine  Coniferous         0.287262       0.111006        0.515767   \n1042        Pine  Coniferous         0.419886       0.382532        0.171516   \n1239       Birch  Coniferous         0.506372       0.193378        0.245580   \n1027        Pine  Coniferous         0.370206       0.176520        0.319970   \n1295      Spruce  Coniferous         0.334030       0.357836        0.238270   \n1244        Pine  Coniferous         0.677940       0.199270        0.089548   \n33         Birch   Deciduous         0.102630       0.815936        0.068432   \n1094      Spruce  Coniferous         0.420922       0.366612        0.153698   \n1304      Spruce   Deciduous         0.309796       0.086894        0.521168   \n1005        Pine  Coniferous         0.359730       0.320182        0.232296   \n1238        Pine  Coniferous         0.321626       0.357192        0.125222   \n\n      aspen_rel_pred  conif_rel_pred  decid_rel_pred dom_species_pred  \\\nid                                                                      \n1039        0.017088        0.808606        0.174612           Spruce   \n1267        0.083194        0.520912        0.366450           Spruce   \n1289        0.052106        0.400616        0.533424            Birch   \n1079        0.041948        0.597190        0.256800             Pine   \n1254        0.012896        0.856914        0.118506           Spruce   \n1097        0.031688        0.449328        0.489346            Birch   \n1074        0.013184        0.943290        0.054862           Spruce   \n1034        0.021632        0.811146        0.170137             Pine   \n1152        0.061764        0.499292        0.363984            Birch   \n1253        0.140988        0.348658        0.575562            Birch   \n1168        0.050620        0.721573        0.202132           Spruce   \n1219        0.039486        0.413576        0.543520            Birch   \n1042        0.008210        0.826284        0.152656           Spruce   \n1239        0.029062        0.683486        0.298348           Spruce   \n1027        0.080154        0.534996        0.388833           Spruce   \n1295        0.046136        0.720440        0.262710             Pine   \n1244        0.024264        0.858502        0.136934           Spruce   \n33          0.000192        0.923648        0.069224             Pine   \n1094        0.020092        0.800988        0.175300             Pine   \n1304        0.030638        0.365320        0.570154            Birch   \n1005        0.058418        0.621586        0.351244           Spruce   \n1238        0.021840        0.638622        0.150190           Spruce   \n\n     dom_type_pred  conif_proba  decid_proba  spruce_proba  pine_proba  \\\nid                                                                       \n1039    Coniferous        0.964        0.036         0.920       0.066   \n1267    Coniferous        0.694        0.306         0.548       0.156   \n1289     Deciduous        0.348        0.652         0.216       0.164   \n1079    Coniferous        0.826        0.174         0.326       0.476   \n1254    Coniferous        0.942        0.058         0.666       0.238   \n1097     Deciduous        0.386        0.614         0.254       0.060   \n1074    Coniferous        1.000        0.000         0.972       0.018   \n1034    Coniferous        0.908        0.092         0.430       0.430   \n1152    Coniferous        0.678        0.322         0.344       0.136   \n1253     Deciduous        0.332        0.668         0.312       0.070   \n1168    Coniferous        0.668        0.332         0.496       0.246   \n1219     Deciduous        0.346        0.654         0.198       0.072   \n1042    Coniferous        0.918        0.082         0.486       0.410   \n1239    Coniferous        0.674        0.326         0.550       0.158   \n1027    Coniferous        0.652        0.348         0.364       0.210   \n1295    Coniferous        0.790        0.210         0.324       0.458   \n1244    Coniferous        0.994        0.006         0.770       0.152   \n33      Coniferous        0.970        0.030         0.004       0.978   \n1094    Coniferous        0.946        0.054         0.454       0.482   \n1304     Deciduous        0.310        0.690         0.212       0.056   \n1005    Coniferous        0.864        0.136         0.396       0.374   \n1238    Coniferous        0.778        0.222         0.540       0.336   \n\n      birch_proba  aspen_proba  \nid                              \n1039        0.014        0.000  \n1267        0.250        0.046  \n1289        0.564        0.056  \n1079        0.158        0.040  \n1254        0.092        0.004  \n1097        0.654        0.032  \n1074        0.006        0.004  \n1034        0.120        0.020  \n1152        0.452        0.068  \n1253        0.414        0.204  \n1168        0.174        0.084  \n1219        0.712        0.018  \n1042        0.100        0.004  \n1239        0.244        0.048  \n1027        0.354        0.072  \n1295        0.202        0.016  \n1244        0.074        0.004  \n33          0.014        0.004  \n1094        0.060        0.004  \n1304        0.682        0.050  \n1005        0.196        0.034  \n1238        0.088        0.036  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>spruce_rel</th>\n      <th>pine_rel</th>\n      <th>birch_rel</th>\n      <th>aspen_rel</th>\n      <th>conif_rel</th>\n      <th>decid_rel</th>\n      <th>dom_species</th>\n      <th>dom_type</th>\n      <th>spruce_rel_pred</th>\n      <th>pine_rel_pred</th>\n      <th>birch_rel_pred</th>\n      <th>aspen_rel_pred</th>\n      <th>conif_rel_pred</th>\n      <th>decid_rel_pred</th>\n      <th>dom_species_pred</th>\n      <th>dom_type_pred</th>\n      <th>conif_proba</th>\n      <th>decid_proba</th>\n      <th>spruce_proba</th>\n      <th>pine_proba</th>\n      <th>birch_proba</th>\n      <th>aspen_proba</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1039</th>\n      <td>0.391</td>\n      <td>0.161</td>\n      <td>0.430</td>\n      <td>0.000</td>\n      <td>0.552</td>\n      <td>0.430</td>\n      <td>Birch</td>\n      <td>Coniferous</td>\n      <td>0.692274</td>\n      <td>0.123000</td>\n      <td>0.153034</td>\n      <td>0.017088</td>\n      <td>0.808606</td>\n      <td>0.174612</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.964</td>\n      <td>0.036</td>\n      <td>0.920</td>\n      <td>0.066</td>\n      <td>0.014</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>1267</th>\n      <td>0.383</td>\n      <td>0.000</td>\n      <td>0.572</td>\n      <td>0.000</td>\n      <td>0.383</td>\n      <td>0.572</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.501784</td>\n      <td>0.131618</td>\n      <td>0.232284</td>\n      <td>0.083194</td>\n      <td>0.520912</td>\n      <td>0.366450</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.694</td>\n      <td>0.306</td>\n      <td>0.548</td>\n      <td>0.156</td>\n      <td>0.250</td>\n      <td>0.046</td>\n    </tr>\n    <tr>\n      <th>1289</th>\n      <td>0.339</td>\n      <td>0.109</td>\n      <td>0.302</td>\n      <td>0.216</td>\n      <td>0.448</td>\n      <td>0.518</td>\n      <td>Spruce</td>\n      <td>Deciduous</td>\n      <td>0.213932</td>\n      <td>0.197364</td>\n      <td>0.467649</td>\n      <td>0.052106</td>\n      <td>0.400616</td>\n      <td>0.533424</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.348</td>\n      <td>0.652</td>\n      <td>0.216</td>\n      <td>0.164</td>\n      <td>0.564</td>\n      <td>0.056</td>\n    </tr>\n    <tr>\n      <th>1079</th>\n      <td>0.031</td>\n      <td>0.000</td>\n      <td>0.151</td>\n      <td>0.799</td>\n      <td>0.031</td>\n      <td>0.950</td>\n      <td>Aspen</td>\n      <td>Deciduous</td>\n      <td>0.346982</td>\n      <td>0.335906</td>\n      <td>0.202276</td>\n      <td>0.041948</td>\n      <td>0.597190</td>\n      <td>0.256800</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.826</td>\n      <td>0.174</td>\n      <td>0.326</td>\n      <td>0.476</td>\n      <td>0.158</td>\n      <td>0.040</td>\n    </tr>\n    <tr>\n      <th>1254</th>\n      <td>0.151</td>\n      <td>0.645</td>\n      <td>0.188</td>\n      <td>0.000</td>\n      <td>0.796</td>\n      <td>0.188</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.550202</td>\n      <td>0.293982</td>\n      <td>0.125520</td>\n      <td>0.012896</td>\n      <td>0.856914</td>\n      <td>0.118506</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.942</td>\n      <td>0.058</td>\n      <td>0.666</td>\n      <td>0.238</td>\n      <td>0.092</td>\n      <td>0.004</td>\n    </tr>\n    <tr>\n      <th>1097</th>\n      <td>0.546</td>\n      <td>0.000</td>\n      <td>0.454</td>\n      <td>0.000</td>\n      <td>0.546</td>\n      <td>0.454</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.353554</td>\n      <td>0.096138</td>\n      <td>0.480882</td>\n      <td>0.031688</td>\n      <td>0.449328</td>\n      <td>0.489346</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.386</td>\n      <td>0.614</td>\n      <td>0.254</td>\n      <td>0.060</td>\n      <td>0.654</td>\n      <td>0.032</td>\n    </tr>\n    <tr>\n      <th>1074</th>\n      <td>0.162</td>\n      <td>0.539</td>\n      <td>0.299</td>\n      <td>0.000</td>\n      <td>0.701</td>\n      <td>0.299</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.758162</td>\n      <td>0.168366</td>\n      <td>0.058364</td>\n      <td>0.013184</td>\n      <td>0.943290</td>\n      <td>0.054862</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.972</td>\n      <td>0.018</td>\n      <td>0.006</td>\n      <td>0.004</td>\n    </tr>\n    <tr>\n      <th>1034</th>\n      <td>0.846</td>\n      <td>0.000</td>\n      <td>0.154</td>\n      <td>0.000</td>\n      <td>0.846</td>\n      <td>0.154</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.400772</td>\n      <td>0.390150</td>\n      <td>0.158930</td>\n      <td>0.021632</td>\n      <td>0.811146</td>\n      <td>0.170137</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.908</td>\n      <td>0.092</td>\n      <td>0.430</td>\n      <td>0.430</td>\n      <td>0.120</td>\n      <td>0.020</td>\n    </tr>\n    <tr>\n      <th>1152</th>\n      <td>0.265</td>\n      <td>0.385</td>\n      <td>0.350</td>\n      <td>0.000</td>\n      <td>0.650</td>\n      <td>0.350</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.367260</td>\n      <td>0.129524</td>\n      <td>0.309858</td>\n      <td>0.061764</td>\n      <td>0.499292</td>\n      <td>0.363984</td>\n      <td>Birch</td>\n      <td>Coniferous</td>\n      <td>0.678</td>\n      <td>0.322</td>\n      <td>0.344</td>\n      <td>0.136</td>\n      <td>0.452</td>\n      <td>0.068</td>\n    </tr>\n    <tr>\n      <th>1253</th>\n      <td>0.147</td>\n      <td>0.000</td>\n      <td>0.145</td>\n      <td>0.000</td>\n      <td>0.147</td>\n      <td>0.145</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.362770</td>\n      <td>0.091748</td>\n      <td>0.357904</td>\n      <td>0.140988</td>\n      <td>0.348658</td>\n      <td>0.575562</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.332</td>\n      <td>0.668</td>\n      <td>0.312</td>\n      <td>0.070</td>\n      <td>0.414</td>\n      <td>0.204</td>\n    </tr>\n    <tr>\n      <th>1168</th>\n      <td>0.000</td>\n      <td>0.396</td>\n      <td>0.604</td>\n      <td>0.000</td>\n      <td>0.396</td>\n      <td>0.604</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.613394</td>\n      <td>0.211012</td>\n      <td>0.094128</td>\n      <td>0.050620</td>\n      <td>0.721573</td>\n      <td>0.202132</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.668</td>\n      <td>0.332</td>\n      <td>0.496</td>\n      <td>0.246</td>\n      <td>0.174</td>\n      <td>0.084</td>\n    </tr>\n    <tr>\n      <th>1219</th>\n      <td>0.028</td>\n      <td>0.792</td>\n      <td>0.180</td>\n      <td>0.000</td>\n      <td>0.820</td>\n      <td>0.180</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.287262</td>\n      <td>0.111006</td>\n      <td>0.515767</td>\n      <td>0.039486</td>\n      <td>0.413576</td>\n      <td>0.543520</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.346</td>\n      <td>0.654</td>\n      <td>0.198</td>\n      <td>0.072</td>\n      <td>0.712</td>\n      <td>0.018</td>\n    </tr>\n    <tr>\n      <th>1042</th>\n      <td>0.137</td>\n      <td>0.461</td>\n      <td>0.084</td>\n      <td>0.000</td>\n      <td>0.598</td>\n      <td>0.084</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.419886</td>\n      <td>0.382532</td>\n      <td>0.171516</td>\n      <td>0.008210</td>\n      <td>0.826284</td>\n      <td>0.152656</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.918</td>\n      <td>0.082</td>\n      <td>0.486</td>\n      <td>0.410</td>\n      <td>0.100</td>\n      <td>0.004</td>\n    </tr>\n    <tr>\n      <th>1239</th>\n      <td>0.269</td>\n      <td>0.189</td>\n      <td>0.322</td>\n      <td>0.000</td>\n      <td>0.458</td>\n      <td>0.322</td>\n      <td>Birch</td>\n      <td>Coniferous</td>\n      <td>0.506372</td>\n      <td>0.193378</td>\n      <td>0.245580</td>\n      <td>0.029062</td>\n      <td>0.683486</td>\n      <td>0.298348</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.674</td>\n      <td>0.326</td>\n      <td>0.550</td>\n      <td>0.158</td>\n      <td>0.244</td>\n      <td>0.048</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td>0.129</td>\n      <td>0.570</td>\n      <td>0.299</td>\n      <td>0.000</td>\n      <td>0.699</td>\n      <td>0.299</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.370206</td>\n      <td>0.176520</td>\n      <td>0.319970</td>\n      <td>0.080154</td>\n      <td>0.534996</td>\n      <td>0.388833</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.652</td>\n      <td>0.348</td>\n      <td>0.364</td>\n      <td>0.210</td>\n      <td>0.354</td>\n      <td>0.072</td>\n    </tr>\n    <tr>\n      <th>1295</th>\n      <td>0.539</td>\n      <td>0.346</td>\n      <td>0.115</td>\n      <td>0.000</td>\n      <td>0.885</td>\n      <td>0.115</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.334030</td>\n      <td>0.357836</td>\n      <td>0.238270</td>\n      <td>0.046136</td>\n      <td>0.720440</td>\n      <td>0.262710</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.790</td>\n      <td>0.210</td>\n      <td>0.324</td>\n      <td>0.458</td>\n      <td>0.202</td>\n      <td>0.016</td>\n    </tr>\n    <tr>\n      <th>1244</th>\n      <td>0.048</td>\n      <td>0.446</td>\n      <td>0.423</td>\n      <td>0.060</td>\n      <td>0.494</td>\n      <td>0.483</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.677940</td>\n      <td>0.199270</td>\n      <td>0.089548</td>\n      <td>0.024264</td>\n      <td>0.858502</td>\n      <td>0.136934</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.994</td>\n      <td>0.006</td>\n      <td>0.770</td>\n      <td>0.152</td>\n      <td>0.074</td>\n      <td>0.004</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>0.388</td>\n      <td>0.033</td>\n      <td>0.509</td>\n      <td>0.070</td>\n      <td>0.421</td>\n      <td>0.579</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.102630</td>\n      <td>0.815936</td>\n      <td>0.068432</td>\n      <td>0.000192</td>\n      <td>0.923648</td>\n      <td>0.069224</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.970</td>\n      <td>0.030</td>\n      <td>0.004</td>\n      <td>0.978</td>\n      <td>0.014</td>\n      <td>0.004</td>\n    </tr>\n    <tr>\n      <th>1094</th>\n      <td>0.516</td>\n      <td>0.000</td>\n      <td>0.484</td>\n      <td>0.000</td>\n      <td>0.516</td>\n      <td>0.484</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.420922</td>\n      <td>0.366612</td>\n      <td>0.153698</td>\n      <td>0.020092</td>\n      <td>0.800988</td>\n      <td>0.175300</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.946</td>\n      <td>0.054</td>\n      <td>0.454</td>\n      <td>0.482</td>\n      <td>0.060</td>\n      <td>0.004</td>\n    </tr>\n    <tr>\n      <th>1304</th>\n      <td>0.344</td>\n      <td>0.000</td>\n      <td>0.229</td>\n      <td>0.205</td>\n      <td>0.344</td>\n      <td>0.434</td>\n      <td>Spruce</td>\n      <td>Deciduous</td>\n      <td>0.309796</td>\n      <td>0.086894</td>\n      <td>0.521168</td>\n      <td>0.030638</td>\n      <td>0.365320</td>\n      <td>0.570154</td>\n      <td>Birch</td>\n      <td>Deciduous</td>\n      <td>0.310</td>\n      <td>0.690</td>\n      <td>0.212</td>\n      <td>0.056</td>\n      <td>0.682</td>\n      <td>0.050</td>\n    </tr>\n    <tr>\n      <th>1005</th>\n      <td>0.029</td>\n      <td>0.813</td>\n      <td>0.156</td>\n      <td>0.000</td>\n      <td>0.842</td>\n      <td>0.156</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.359730</td>\n      <td>0.320182</td>\n      <td>0.232296</td>\n      <td>0.058418</td>\n      <td>0.621586</td>\n      <td>0.351244</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.864</td>\n      <td>0.136</td>\n      <td>0.396</td>\n      <td>0.374</td>\n      <td>0.196</td>\n      <td>0.034</td>\n    </tr>\n    <tr>\n      <th>1238</th>\n      <td>0.000</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>Pine</td>\n      <td>Coniferous</td>\n      <td>0.321626</td>\n      <td>0.357192</td>\n      <td>0.125222</td>\n      <td>0.021840</td>\n      <td>0.638622</td>\n      <td>0.150190</td>\n      <td>Spruce</td>\n      <td>Coniferous</td>\n      <td>0.778</td>\n      <td>0.222</td>\n      <td>0.540</td>\n      <td>0.336</td>\n      <td>0.088</td>\n      <td>0.036</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Plot [SHAP values](https://github.com/slundberg/shap) for predictions"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:01.779440Z",
          "end_time": "2020-04-06T11:53:05.689021Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import shap\nimport random\nshap.initjs()\n\nexplainer = shap.TreeExplainer(reg_spe)\nshap_values = explainer.shap_values(test_X)",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return 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r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\nn===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "Setting feature_perturbation = \"tree_path_dependent\" because no background data was given.\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-06T11:53:05.691288Z",
          "end_time": "2020-04-06T11:53:07.239456Z"
        },
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "for s, i in zip(species, range(4)):\n    print(f'Summary plot for {s} proportion')\n    shap.summary_plot(shap_values[i], test_X)",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Summary plot for Spruce proportion\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x684 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": "Summary plot for Pine proportion\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x684 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": "Summary plot for Birch proportion\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x684 with 2 Axes>",
            "image/png": 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

## Trees_sentinel_als.ipynb

      
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